Deep Learning
Deep learning, a subfield of machine learning, focuses on training artificial neural networks with multiple layers to extract complex patterns from data. Current research emphasizes improving model robustness against noisy or adversarial inputs, exploring efficient architectures like Vision Transformers and convolutional LSTMs for various tasks (e.g., image classification, time series forecasting), and integrating physics-informed approaches for enhanced interpretability and reliability. These advancements are significantly impacting diverse fields, from automated industrial inspection and medical image analysis to improved weather forecasting and more efficient content moderation systems.
Papers
Science Time Series: Deep Learning in Hydrology
Junyang He, Ying-Jung Chen, Anushka Idamekorala, Geoffrey Fox
Stool Recognition for Colorectal Cancer Detection through Deep Learning
Glenda Hui En Tan (1), Goh Xin Ru Karin (2), Shen Bingquan (3) ((1) Carnegie Mellon University, (2) London School of Economics and Political Science, (3) DSO National Laboratories Singapore)
Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data
Yuhao Gong, Yuchen Zhang, Fei Wang, Chi-Han Lee
Tackling domain generalization for out-of-distribution endoscopic imaging
Mansoor Ali Teevno, Gilberto Ochoa-Ruiz, Sharib Ali
A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts
Jiaxin Lu, Yongqing Liang, Huijun Han, Jiacheng Hua, Junfeng Jiang, Xin Li, Qixing Huang
On the Regularization of Learnable Embeddings for Time Series Processing
Luca Butera, Giovanni De Felice, Andrea Cini, Cesare Alippi
An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique
Maksuda Akter, Rabea Khatun, Md. Alamin Talukder, Md. Manowarul Islam, Md. Ashraf Uddin
Investigating the Capabilities of Deep Learning for Processing and Interpreting One-Shot Multi-offset GPR Data: A Numerical Case Study for Lunar and Martian Environments
Iraklis Giannakis, Craig Warren, Antonios Giannopoulos, Georgios Leontidis, Yan Su, Feng Zhou, Javier Martin-Torres, Nectaria Diamanti
Debiasing Mini-Batch Quadratics for Applications in Deep Learning
Lukas Tatzel, Bálint Mucsányi, Osane Hackel, Philipp Hennig
Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review
Ahmad Obeid, Said Boumaraf, Anabia Sohail, Taimur Hassan, Sajid Javed, Jorge Dias, Mohammed Bennamoun, Naoufel Werghi
Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts
Felix Krones, Ben Walker, Terry Lyons, Adam Mahdi
Deep Learning Applications in Medical Image Analysis: Advancements, Challenges, and Future Directions
Aimina Ali Eli, Abida Ali
Self Supervised Deep Learning for Robot Grasping
Danyal Saqib, Wajahat Hussain
AutoAL: Automated Active Learning with Differentiable Query Strategy Search
Yifeng Wang, Xueying Zhan, Siyu Huang
CrystalX: Ultra-Precision Crystal Structure Resolution and Error Correction Using Deep Learning
Kaipeng Zheng, Weiran Huang, Wanli Ouyang, Han-Sen Zhong, Yuqiang Li
Label-free prediction of fluorescence markers in bovine satellite cells using deep learning
Sania Sinha, Aarham Wasit, Won Seob Kim, Jongkyoo Kim, Jiyoon Yi
Enhanced Prompt-leveraged Weakly Supervised Cancer Segmentation based on Segment Anything
Joonhyeon Song, Seohwan Yun, Seongho Yoon, Joohyeok Kim, Sangmin Lee
Object Pose Estimation Using Implicit Representation For Transparent Objects
Varun Burde, Artem Moroz, Vit Zeman, Pavel Burget